1. Field of the Invention
Example embodiments of the present invention are directed to methods for improving performance of a wireless network.
2. Description of the Conventional Art
Overall network performance of conventional wireless networks may vary based on wireless user traffic loading and/or user traffic patterns (herein after referred to as user traffic data) over different time periods. For example, within a single day, user traffic over a conventional wireless network may vary significantly from one hour to the next, which may subsequently result in a substantial variation in the overall performance of the network from hour-to-hour. Similarly, over extended periods of time, seasonal variations from month-to-month may also have an impact on the overall performance of a conventional wireless network. In each of these examples, different network parameters may be required from, for example, hour-to-hour or month-to-month to achieve a desired level of network performance (e.g., an optimum level of network performance).
Overall performance of a network for a particular set of network parameters may be characterized by a vector with two components, one representing network coverage and another representing network capacity. Network coverage may represent a likelihood of probability of service under load, and may be weighted by user traffic density. On the other hand, network capacity may be the amount of user traffic (e.g., within a cell of a wireless communications network), which may be served at a given overall target-blocking rate.
Conventionally, network parameters for the network are determined in the design phase, before the wireless network is implemented. In the design phase, network parameters may utilize design tools that model or predict network performance based on given network parameters (e.g., set by a human network operator) using statistical or other mathematical propagation models. However, the accuracy of these predictions may depend on the accuracy of the propagation models and the precision of modeling the environment, that is, for example, terrain, clutter, etc. Subsequently, inaccuracy in the modeling environment may result in inaccurate predictions in, for example, developing areas (e.g., residential and/or commercial areas). Furthermore, few of these network parameters may be adjusted after implementation, and those that may be adjusted are cost- and/or time-intensive.